In the automotive repair industry, diagnosing vehicle faults typically requires gathering information from a user related to the perceived fault. The repair technician then evaluates the information received and makes a determination as to what vehicle system(s) or component(s) of the vehicle assembly are causing the fault. This approach to vehicle diagnostics is symptom driven, meaning that the description of the fault is not analyzed based on the vehicle system, but instead is generally determined based on the perceived fault a customer provides. Accurate translation of the symptom to a vehicle system or component is critical for efficient and accurate diagnosis of problems. This becomes particularly important with respect to modern vehicles because of complex integrated vehicle electronic systems. In addition, it is often impractical to narrow down an excessively large list of possible symptoms to a smaller list of possible systems. Instead, the list of possible systems should be narrowed down because it is exceptionally smaller from the start.
To take the observed or described operational characteristics of a vehicle and accurately translate that information into a specific vehicle system is not always intuitive. For example, drivability symptoms (e.g., low power, poor performance, harsh shifting, slow starting, etc.) may be associated with multiple vehicle systems. Accordingly, an application that assists a user in translating a symptom to a system and to provide a reasonably accurate and comprehensive list of vehicle systems likely to be associated with the fault would prove useful, and would be an improvement in the art.